caa a22 4500 605469407 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1299-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1299-4 Knowledge-driven path planning for mobile robots: relative state tree [Elektronische Daten] [Yang Chen, Lei Cheng, Huaiyu Wu, Xingang Zhao, Jianda Han] Path planning is important in the field of mobile robot. However, traditional path planning techniques optimize the navigation path solely based on the models of the robot and the environments. Owing to the time-varying environment, the robot is expected to launch the replanning procedure in real-time continuously. It is slow and wastes computing resources for repeated decisions. In this study, a new perspective is adopted which utilizes a knowledge-driven approach for path planning. The concept of relative state tree is proposed to develop an incremental learning method based on a path planning knowledge base. The knowledge library, which stores a collection of the mappings from environmental information to robot decisions, can be established by offline or online learnings. As the robot plans online, its movement is guided by the optimal decision that is retrieved from the library based on the information which matches mostly the current environment. A large number of simulations are executed to verify the proposed method. When comparing to $$k$$ k -d tree, this novel method has shown to use smaller storage space and have higher efficiency. Springer-Verlag Berlin Heidelberg, 2014 Path planning nationallicence Relative state tree nationallicence Hierarchical tree nationallicence Incremental learning nationallicence Autonomous planning nationallicence Chen Yang School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China aut Cheng Lei School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China aut Wu Huaiyu School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China aut Zhao Xingang State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China aut Han Jianda State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 763-773 1432-7643 19:3<763 2015 19 500 https://doi.org/10.1007/s00500-014-1299-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1299-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Yang School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China aut NATIONALLICENCE 700 1- Cheng Lei School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China aut NATIONALLICENCE 700 1- Wu Huaiyu School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China aut NATIONALLICENCE 700 1- Zhao Xingang State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China aut NATIONALLICENCE 700 1- Han Jianda State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 763-773 1432-7643 19:3<763 2015 19 500